Record data on individuals without attempting to influence the
responses. We typically cannot prove anything this way.
Experimental study
Deliberately impose a treatment on individuals and record their
responses. Influential factors can be controlled.
Observational studies of the effect of one variable on another often fail because the
explanatory variable is confounded with lurking variables.
Well-designed experiments take steps to defeat confounding.
Population
The entire group of individuals in which we are interested but can’t usually assess directly.
Parameter
a number describing a characteristic of the population.
Sample
The part of the population we actually examine and for which we do have data
How well the sample represents the population depends on the sample design.
Statistic is a number describing a characteristic of a sample
Bad sampling methods
Convenience sampling & bias
Convenience sampling
Just ask whoever is around.
Bias
Opinions limited to individuals present
Voluntary Response Sampling
Individuals choose to be involved
Bias
Sample design systematically favors a particular outcome.
Good sampling methods
Probability or random sampling
Probability or random sampling
Individuals are randomly selected.
Sampling randomly gets rid of bias.
Simple random sample
(SRS) is made of randomly selected individuals. Each
individual in the population has the same probability of being in the sample. All possible
samples of size n have the same chance of being drawn.
SRS
Simple random sample
How to choose an SRS of size n from a population of size N:
1. Label
2. Table B
3.Stratified random sample
Label
Give each member of the population a numerical label of the same length.
Table B
Read from Table B successive groups of digits of the length you used as labels. Your sample contains the individuals whose labels you find in the table.
Stratified random sample
a series of SRS performed on subgroups of a given
population. The subgroups are chosen to contain all the individuals with a certain characteristic.
The SRS taken within each group in a stratified random sample need not be of the same
size.
Caution about sampling surveys
Nonresponse
Response bias
Wording effects
Undercoverage
Learning about populations from samples
The techniques of inferential statistics allow us to draw inferences or conclusions about a
population from a sample.
Your estimate of the population is only as good as your sampling design Work hard to eliminate biases.
Your sample is only an estimate—and if you randomly sampled again, you would probably get a somewhat different result.